Table 2 The summary of related work.
From: Blockchain framework with IoT device using federated learning for sustainable healthcare systems
S. No. | Methods | Advantages | Limitations |
|---|---|---|---|
1 | ML in Healthcare System (ML-HCS)19 | Ensures secure, transparent, and decentralized data sharing Protects patient privacy Facilitates access management for authorized parties | Requires significant computational power Scalability challenges with large datasets Complex integration with existing healthcare systems |
2 | Hybrid DL Methods (HDLM)21 | Enables fast and accurate processing of complex healthcare data (e.g., medical records, images) Combines DL with traditional ML methods | Requires large amounts of labeled data Complex model training High computational resources needed for real-time analysis |
3 | Big Data Analysis (BDA)23 | Protects patient privacy and ensures the authenticity of transactions Separates sensitive and non-sensitive data Improves research security | Performance may degrade with increased data size Complexity in managing access controls and cryptographic methods Regulatory compliance challenges |
4 | Artificial Neural Network (ANN)25 | Allows for predictive health notifications and therapeutic follow-ups Enhances security for wearable healthcare devices | Vulnerable to attacks if not properly secured Requires expertise to train and implement ANN models effectively Potentially high energy consumption for real-time analysis |
